Exploring the sun with big data

Analytical advances help probe the profound questions of our universe

By Daniel Teachey, SAS Insights Editor

What can we learn from a satellite orbiting 1 million miles from Earth? What is happening on the face of the sun that is turned away from Earth? How does our heliosphere – the magnetic bubble in which our sun and planets reside – interact with interstellar space?

Those are the kinds of questions that confront astrophysicists and astronomers as they explore the mysteries of our universe. One is to solve why the sun’s corona – the outermost layer of its atmosphere – is 300 times hotter than the sun’s surface, which is already 5,600 degrees Celsius (10,000 degrees Fahrenheit). Scientists suspect that energy trapped in magnetic fields somehow causes this intense heating, and coronal loops are probably key in this ongoing investigation.

Coronal loops are bright, dynamic structures that appear as arcs above the sun's surface. They glow from hot plasma and reach temperatures well above 1 million degrees. The electrified plasma flows along the curving lines of powerful magnetic fields. These luminous magnetic arches are associated with solar spots, which add considerably to X-ray and ultraviolet radiation from the outer solar atmosphere – and into the upper atmosphere of Earth. 

The astrophysicist community speculates that when these powerful arches are created, magnetic reconnection occurs – a physical process where magnetic energy is converted to kinetic energy, thermal energy and particle acceleration.

This is the moment that researchers Lars Daldorff and Siavoush Mohammadi want to identify and understand. Daldorff is an atmospheric, oceanic and space sciences research fellow at the University of Michigan and a consultant for NASA’s Goddard Space Flight Center. Mohammadi is a consultant with Infotrek, a Swedish business intelligence and data warehousing company.

They joined forces to tackle a big stumbling block in the discovery process – the crush of data that threatens to drown scientific processes in information and inhibit the ability to transform it into insight and knowledge.

Learn how to manage your analytical life cycle

When physicists use large supercomputers to simulate the sun, it produces massive amounts of data. But the phenomenon of interest is usually located at a specific point in time and space, essentially creating a needle-in-a-haystack situation. What you’re looking for is somewhere in the data, but you usually do not know where or even when in the data it can be found. The researcher then has to slice the data into smaller portions based on qualified guesses of where it might be. 

The problem is that even if you are lucky and just happen to find an interesting phenomenon on your first guess, you can’t be sure that it is the only phenomenon of interest in the data. This means that the time between the gathering of data (from numerical simulations of the sun in this case) and insight about your data becomes very long.

What if you could scan the entire haystack at once to find the needle? What if, after you have found the needle(s), you could simply export the data of interest to do full analysis on only what matters?

Those are the questions Daldorff and Mohammadi sought to answer when they looked to commercial analytics solutions to explore, categorize and display the large amount of solar research project data from the plasma simulations Daldorff had conducted for NASA. Technical advances known as automatic explorative analysis of data – widely used in the business world, such as to create customer intelligence – gained a new role helping scientists seeking understand our universe.

The duo has been using SAS^® Visual Analytics – a big data discovery, interactive exploration and reporting tool that works in memory. Many of the analytical methods used by SAS Visual Analytics are standardized and used for data analytics in numerous industries. The methods for identifying points of interest, finding relevant data relationships, performing analysis, visualizations and creating reports are the same, whether you’re working with business or scientific data.

“Our hope is these results can help with solar magnetic loops research at NASA and, at the same time, our work will show the effectiveness of explorative analysis of data in other data-intensive fields,” said Daldorff and Mohammadi in presenting their work at the 2015 Joint Statistical Meetings in Seattle. “There are numerous possibilities for this new application that could potentially help various types of researchers – in academia, business and science – obtain quicker insights and results from their research’s big data.”